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8 Table 1. Examples of airports by category. Airport Size Characteristic Category Large Medium Small SEA, LGA, BWI, OAK, RDU, PDX, O&D OKC, DAY, LIT Predominant SAN RNO, SDF passenger type DFW, ATL, ORD, STL, MEM, HOU, Connecting -- MSP, IAD CLE ORD, ATL, MIA, STL, MEM, CLE, Legacy SAV, HSV, XNA IAH, IAD SJU Predominant ATL, MDW, OAK, BNA, Low cost BIL, SFB, PHF carrier(s) PHX, BWI, FLL HOU, DAL No predominant LAX, JFK, MCO, MSY, BDL, HPN, MYR, ABE carrier HNL SAT, MHT, SDF LAX, JFK, DFW International SJU GUM, GSN, SFB Predominant YYZ, SFO destinations DEN, DFW, LGA, Domestic All PHF, PNS, BTV BWI LAX, LAS, MCO, SJU, MSY, Leisure ACY, PSP, MYR HNL OGG, RSW Purpose of travel LGA, LAX, DAL, PVD, Business ILM, OKC, DAY JFK, LAS SJC, MCI PDX, DAL, ANC, ALB, MDT, Centralized ATL, DEN Terminal AUS, SDF LIT, SYR configuration STL, SAT, DTW, DFW, Decentralized -- MCO, BWI BNA, YVR ATL, DEN, PDX, BNA, ABQ, TUL, MDT, ALB, Single terminal Landside terminals IAD, YUL ANC, DAL DAY, PHF Multiple terminals DFW, JFK OAK, SAT -- Note: O&D = origin and destination task activities. Differences in quantitative and qualitative re- the pretest were reviewed and the data collection approach search concepts were discussed and folded into strategies by adjusted for the first data collection at DFW over President's the quantitative and qualitative technical team leaders. From Day weekend (February 1518). this, each team member built a strategy to best capture the TransSolutions initially used four methods to understand relevant data needed from their particular discipline. passenger perceptions of level of service in relation to the space available to them and the process time involved in their journey: Data Collection Cities TransSolutions targeted airports from Table 1 that allowed 1. Wait-time studies and observation of queue length corre- assessment of any differences that might exist between air- lated with passenger perception surveys, ports from each characteristic category. Airports that voiced 2. Passenger surveys regarding perception, a specific interest to be considered for data collection (in our 3. Video capture and analysis of dwell time, and online survey) were the first considered for on-site surveying. 4. Ethnographic research. Table 2 details the study airports along with the categories and characteristics each airport represents. The team looked at the results of the DFW test and realized that analysis of the video data would be too time-consuming relative to the number of data points collected. The group re- Data Collection Methodology designed the studies to accomplish the goals of the study in a more efficient manner. Essentially, all of the passenger inter- Initial Approach cepts were converted to a two-person process that could be A pretest of the survey instruments at DFW airport was completed without the use of videographic evidence. The conducted during the week of January 28, 2008. Results from first person would hand the passenger an ID card while ask-

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9 Table 2. Categories and characteristics for subject airports. Predominant Purpose Terminal Airport Airport Name and Airport Predominant Predominant Landside FAA Passenger of Configuration ID Location Size Carrier(s) Destinations Terminals Region Type Travel (per Terminal) Dallas/Fort Worth Domestic/ Leisure, DFW International Airport-- Large Connecting Legacy Decentralized Multiple Southwest international business Dallas, TX Austin-Bergstrom AUS International Airport-- Medium O&D Low cost Domestic Business Centralized Single Southwest Austin, TX Hartsfield-Jackson Legacy, low Domestic/ ATL Atlanta International Large Connecting Business Centralized Single Southern cost international Airport--Atlanta, GA McCarran Leisure, Western- LAS International Airport-- Large O&D None Domestic Centralized Multiple* business Pacific Las Vegas, NV Oakland International Western- OAK Medium O&D Low cost Domestic Business Decentralized Multiple Airport--Oakland, CA Pacific Louisville SDF International Airport-- Medium O&D None Domestic Business Centralized Single Southern Louisville, KY Washington Dulles Domestic/ Leisure, IAD International Airport-- Large Connecting Legacy Centralized Single Eastern international business Dulles, VA *Smaller secondary terminal Note: O&D = origin and destination ing him or her several demographic questions and marking for future calculations of space per passenger. It was not prac- the time of the first interview. When the passenger reached tical to obtain CAD drawings of the airport areas observed. the end of the process, a second interviewer would record the Therefore, the data collectors were careful to determine the intercept time and complete the interview by asking the pas- area that would be used for calculation of LOS based on senger what his or her perception of the process was on a five-point scale (where 1 is excellent and 5 is very bad). In the check-in area, the queue area designated for waiting Another alteration was made with respect to Federal Inspec- and the area designated for check-in service; tion Services (FIS) facilities. It was apparent from the test run In the holdroom, the seating area exclusive of the agent that it was going to be impractical to obtain the proper num- counter, queue, and jet bridge boarding/de-boarding area; ber of escorts with the necessary language skills to fairly mea- and sure the perception of passengers within the FIS facilities. In the baggage claim area, the active claim area, defined as Given this reality, in addition to international flights' arrival 11.5 ft from the face of the baggage claim devices. times being highly variable and the limited availability of the data collection team's resources, the ACRP panel agreed on For the quantitative passenger intercepts, the team con- the cancellation of conducting FIS interviews. ducted surveys of passenger perceptions in various areas of the airport. The areas where intercepts were conducted com- Final Approach prised the entire process-based passenger experience once inside the terminal facilities. After analyzing the results of the initial data collection, the For passengers checking in, the curbside positions, ticket team made the aforementioned adjustments to the scope agent, or kiosk area typically provide their first interaction and techniques and conducted six more full-scale data col- within the facility. Exhibit 1 shows select check-in areas at lections at the following airports between August 10 and various study airports. Due to the short nature of the associ- September 17, 2008: Austin-Bergstrom International Airport ated queues and wait times involved, for the kiosk process, (AUS), Hartsfield-Jackson Atlanta International Airport (ATL), the second interview occurred immediately after the passen- Oakland International Airport (OAK), Louisville International ger completed the kiosk process (but prior to bag drop-off). Airport (SDF), McCarran International Airport (LAS), and All passengers must then proceed to the security screen- Washington Dulles International Airport (IAD). In addition ing checkpoint (SSCP) area, where uniformed Transporta- to DFW, concurrent ethnographic data collections were con- tion Security Administration (TSA) agents conduct a passive ducted at ATL, SDF, and IAD. search of the passengers and their bags and possibly recom- At each airport, all areas selected for data collection were mend them for further screening. The waiting line for this documented. This included taking photographs and making process is actually divided into two areas by an ID check pro- physical surveys of the area to determine the size of the area cedure. Prior to the ID check, passengers generally wait in a

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10 Exhibit 1. Select check-in areas at study airports. ATL Curbside ATL Ticketing/ Delta Air Lines OAK Kiosk single queue (or multiple queues, if the airport separates pas- cepts occurred at the entry and exit points for these facilities sengers by type--such as frequent traveler or preferred sta- (see Exhibit 3). tus), but once the ID check is complete, the passengers gen- The only intercept conducted specifically for arriving erally move into a set of smaller queues in front of each x-ray passengers occurred at baggage claim (see Exhibit 4). It was lane where they begin to divest their belongings. performed similarly to the kiosk process above, although It was important for the research to gauge passengers' per- the first interview with the passengers occurred when they ceptions of the experience prior to their actual interaction first arrived to the claim area, and the final interview occurred with the agent for that process, so our data collectors were sta- after the final passengers claimed their bags and were ready tioned at the front and back of each process queue under re- to depart. view. For the SSCP, the interviews were done solely in the queues prior to the ID check process (see Exhibit 2). Once the passenger exits security, the remaining areas of Data Collection Schedule interest are not agent- or processor-based. Passengers move The choice of data collection times and locations was driven through the corridors and concourses, and those waiting for by the desire to get the most quantitative data possible. Specifi- an automated people mover (APM) (for airports that have cally, departing passenger demand was assumed to be heaviest them) or waiting for boarding while in a holdroom do have during the early morning hours and late afternoon. Arriving- an element of waiting involved (or the potential for conges- passenger demand was assumed to peak during the early to late tion that slows movement, in the case of a crowded concourse evening. For those reasons, passenger check-in, SSCP screen- or holdroom). For that reason, the interviews for these inter- ing, and holdroom intercepts were planned for the early morn- ing and early evening, while bag claim intercepts for arriving passengers were scheduled for the 4 through 6 p.m. time frame, Exhibit 2. Security screening checkpoint at IAD. as shown in Table 3. A total of three days' worth of data were taken for each airport, from Sunday afternoon to Wednesday morning. We expected this would provide a good cross section of par- ticipants that would potentially include leisure and business travelers. Once a current flight schedule was obtained for the candi- date airport, these times were modified to fit the actual pas- senger pattern at that specific airport. In order to maximize the total number of responses, airlines with a larger passen- ger share at a particular airport were scheduled for Monday collection since that day is traditionally a heavier demand day across the system. Statistically valid survey design tech- niques were used to ensure a representative sample of air- lines for each airport with regard to check-in facilities and bag claim locations.

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11 Exhibit 3. Corridor, APM, and holdroom intercept areas. SDF Corridor DFW APM area AUS Holdroom Data Collection Procedure Additionally, the data collectors noted the start and end time of the processing and the number of people in the area Several questions were asked of the subjects to help classify of interest at the time of the observation. the perception rating at the beginning of the process. For check-in locations, passengers were interviewed prior For the check-in and security processes, the passengers to joining the queue in front of the agents or kiosks. They were interviewed when they entered the queue and again were asked the following questions: before they talked to the check-in agent or the ID checker prior to the security checkpoint. How many people are in the traveling party? The processing time for the holdroom was calculated from How many bags are you checking? initial passenger arrival at the holdroom area to the time How many carts are you using? they entered the boarding queue. Is your trip primarily for business or leisure purposes? The processing time for the APM stations was calculated Is your trip to a domestic or international location? from passenger arrival at the platform to subsequent board- ing of the train. For other airport locations, the passenger was asked The calculation of the processing time for the corridor was completed by positioning the data collectors approximately Is your trip primarily for business or leisure purposes? 100 to 200 ft apart and intercepting passengers moving in Is your trip to a domestic or international location? the dominating flow direction. The data collectors were positioned so that there were no intervening holdrooms, restrooms, or shops to divert passengers. Exhibit 4. Baggage claim at LAS. The team thus collected objective passenger processing data, including wait time, number of passengers in queue, and square feet per passenger. Two instruments were used to con- duct the survey in each case. First, a personal digital assistant (PDA) was used to accurately record responses to each of the questions posed by the interviewer. Second, a colored, num- bered card was handed to the interview subject at the beginning of the queue and requested to be returned once the passenger reached the other interviewer at the head of the queue. By rec- onciling the time stamps for the particular passenger, the time spent waiting in process could be calculated. Passengers were asked at the end of the process to rank their experience on the scale shown in Table 4. Finally, ethnographic research was conducted in areas of the airport to record in-depth passenger perceptions. Ethno- graphic data were collected on focused passenger processing

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12 Table 3. Generic data collection plan. Sunday Monday Tuesday Wednesday Ticketing Ticketing 5:00 a.m. 5:00 a.m. 5:00 a.m. Concourse Concourse Kiosk to to to Holdroom Holdroom SSCP 7:00 a.m. 7:00 a.m. 7:00 a.m. APM APM Kiosk Morning n/a Ticketing Ticketing 7:00 a.m. 7:00 a.m. 7:00 a.m. Concourse Concourse Kiosk to to to Holdroom Holdroom SSCP 9:00 a.m. 9:00 a.m. 9:00 a.m. APM APM Holdroom Flight in 9:00 a.m. 9:00 a.m. 9:00 a.m. Afternoon As required to arrive no later to Break to Break to Flight out than 2:00 p.m. 4:00 p.m. 4:00 p.m. 12:00 p.m. Kiosk Curbside 4:00 p.m. 4:00 p.m. 4:00 p.m. Curbside SSCP SSCP to to to SSCP Concourse Bag claim 6:00 p.m. 6:00 p.m. 6:00 p.m. Bag claim Ticketing Evening Kiosk n/a 6:00 p.m. 6:00 p.m. 6:00 p.m. SSCP to to Bag claim to Bag claim Holdroom 8:00 p.m. 8:00 p.m. 8:00 p.m. Ticketing at the ticket counter, security screening checkpoint, and bag- from solo business travelers to large families on vacation. gage claim. In the holdrooms, research was conducted with T-tests were performed to determine if there was any signif- the objective of evaluating the passengers' holistic view of icant difference between the responses for large and small air- their airport experience. port types for a given factor before it was aggregated as shown As the data collection process progressed from airport to in Table 6. Since for most areas there was no significant dif- airport, captured data (quantitative) points were cataloged as ference, we felt it appropriate to aggregate the data to attempt detailed in Table 5 to ensure coverage of major airlines and to form a national standard. facility types. Using a standard statistical analysis approach, the null hypothesis for the t-test was defined. For this case, our null hypothesis was that the actual average perception ratings Data Analysis Approach are equal for the two populations under consideration in For each condition, the data groupings were compared to each test (Data Group A and Data Group B). The calculated determine if there was a difference in the average perception p-value represents a probability that corresponds to this values using a standard statistical technique known as the t-test. question: The team verified that the underlying assumptions (regarding For an experiment of this magnitude, if the true populations sample sizes, normalcy, and independence of the data points) studied really do have the same mean value, what is the proba- for the use of this test were validated. bility of observing at least as large a difference between sample It was further assumed for the purposes of this analysis that means as was actually observed? the data collected were sufficiently representative of the na- If the p-value is less than a certain threshold (traditionally tional air-traveling public as a whole. We chose seven airports .05, or 5%), then we reject the null hypothesis previously that varied in size, geographic location, and function, and col- stated and conclude that there likely is a difference between lected data from passengers of many demographics and types, the average perception for the two data sets. Essentially, the lower the p-value, the more certain we are that the observed Table 4. difference between data groupings is statistically significant. Passenger If we are unable to reject the null hypothesis, we cannot say perception scale. with confidence that there is no difference; we were just not able to detect it with this experiment. Scale Description Once it is determined that the differences in the average 1 Excellent perceptions are significant, we can examine the trends 2 Good within each separate data group to determine when the 3 Acceptable 4 Bad average perception is likely to be worse than acceptable (in 5 Very bad our case when the average perception rating = 3.0). This

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13 Table 5. Catalog of captured data points, IAD. Collection Location for Data Points Kiosk Staffed No. of Curbside Baggage Hold- Airline Kiosks Bag Agent Check-In Claim SSCP Corridor room Drop Check-In Unknown 0 0 0 11 0 205 0 0 American 8 0 0 0 0 0 0 25 Delta 12 0 0 0 0 0 0 25 JetBlue 4 0 60 0 40 0 0 22 Southwest 6 0 0 0 0 0 0 25 United domestic 50 39 60 27 150 0 100 35 United international 22 0 73 0 0 0 0 39 Virgin America Airlines 6 0 0 0 0 0 0 25 Total Data Points Captured 39 193 38 190 205 100 196 Table 6. Measure of perception difference by airport size. Small/Medium Large Airport Significant Functions Airport Average p-value Average Perception Difference Perception Curbside 1.19 1.93 0.001 Yes Ticketing 2.22 2.42 0.159 No Kiosk 2.12 2.16 0.832 No Bag drop 2.19 2.16 0.893 No SSCP 1.99 1.89 0.310 No Corridor 1.72 2.07 0.001 Yes Holdroom 1.79 1.97 0.056 No Bag claim 2.06 2.21 0.012 Yes will become the nominal "turning point" (TP) of the envi- passenger or wait time). If there are no such significant ronmental factor, the point where we find that the average trends available for a factor, then we cannot reliably deter- perception switches from better than acceptable to less than mine a TP and thus cannot develop a design metric for that acceptable based on the factor of interest (i.e., space per quantitative factor.